System and method for predicting endometrium receptivity

Abstract

Methods and systems for predicting endometrium receptivity are disclosed, the method include: maintaining a data set representing a neural network having a plurality of weights; obtaining a first image of an endometrium with a first timestamp; extracting a first set of target endometrium features from the first image; and generating, using the neural network and based on the first set of target endometrium features, a predicted value indicating a endometrium receptivity of the endometrium in the first image.

1 . A computer-implemented system for predicting endometrium receptivity, comprising:
 a processor; and   a memory coupled to the processor and storing processor-executable instructions that, when executed, configure the processor to:
 maintain a data set representing a neural network having a plurality of weights; 
 obtain a first image of an endometrium with a first timestamp; 
 extract a first set of target endometrium features from the first image; and 
 generate, using the neural network and based on the first set of target endometrium features, a predicted value indicating an endometrium receptivity of the endometrium in the first image. 
   
     
     
         2 . The system of  claim 1 , wherein the processor-executable instructions, when executed, further configure the processor to:
 generate a value representative of a likelihood of a successful embryo implantation.   
     
     
         3 . The system of  claim 1 , wherein the first set of target endometrium features comprises at least one of: a thickness of the endometrium, a length of the endometrium, a surface area of the endometrium, and a pattern of the endometrium. 
     
     
         4 . The system of  claim 3 , wherein the pattern of the endometrium comprises a trilaminar pattern. 
     
     
         5 . The system of  claim 1 , wherein the processor-executable instructions, when executed, configure the processor to:
 receive a second image of the endometrium with a second timestamp;   extract a second set of target endometrium features from the second image; and   generate, using the neural network and based on the first and second sets of target endometrium features, the predicted value indicating the endometrium receptivity of the endometrium.   
     
     
         6 . The system of  claim 5 , wherein the second set of target endometrium features comprises at least one of: a thickness of the endometrium, a length of the endometrium, a surface area of the endometrium, and a trilaminar pattern of the endometrium. 
     
     
         7 . (canceled) 
     
     
         8 . The system of  claim 5 , wherein the processor-executable instructions, when executed, configure the processor to:
 determine a difference between the first image and the second image; and   analyze the difference to generate the predicted value indicating the endometrium receptivity of the endometrium.   
     
     
         9 . (canceled) 
     
     
         10 . (canceled) 
     
     
         11 . The system of  claim 1 , wherein the predicted value indicating the endometrium receptivity comprises a probability value. 
     
     
         12 . (canceled) 
     
     
         13 . The system of  claim 1 , wherein the neural network is trained based on a set of training data comprising:
 a plurality of ultrasound images of one or more endometria, each of the plurality of ultrasound images showing a respective endometrium; and   for each of the plurality of ultrasound images, a respective label indicating an outcome of a respective embryo implantation in the respective endometrium in the respective ultrasound image.   
     
     
         14 . The system of  claim 13 , wherein each of the plurality of ultrasound images is associated with training data comprising a blastocyst quality of an embryo transferred into a respective endometrial cavity in the respective ultrasound image. 
     
     
         15 . (canceled) 
     
     
         16 . (canceled) 
     
     
         17 . A computer-implemented method for predicting endometrium receptivity, the method comprising:
 maintaining a data set representing a neural network having a plurality of weights;   obtaining a first image of an endometrium with a first timestamp;   extracting a first set of target endometrium features from the first image; and   generating, using the neural network and based on the first set of target endometrium features, a predicted value indicating an endometrium receptivity of the endometrium in the first image.   
     
     
         18 . The method of  claim 17 , further comprising:
 generating a value representative of a likelihood of a successful embryo implantation.   
     
     
         19 . The method of  claim 17 , wherein the first set of target endometrium features comprises at least one of: a thickness of the endometrium, a length of the endometrium, a surface area of the endometrium, and a pattern of the endometrium. 
     
     
         20 . The method of  claim 19 , wherein the pattern of the endometrium comprises a trilaminar pattern. 
     
     
         21 . The method of  claim 17 , further comprising:
 receiving a second image of the endometrium with a second timestamp;   extracting a second set of target endometrium features from the second image; and   generating, using the neural network and based on the first and second sets of target endometrium features, the predicted value indicating the endometrium receptivity of the endometrium.   
     
     
         22 . The method of  claim 21 , wherein the second set of target endometrium features comprises at least one of: a thickness of the endometrium, a length of the endometrium, a surface area of the endometrium, and a trilaminar pattern of the endometrium. 
     
     
         23 . The method of  claim 21 , further comprising:
 determining a difference between the first image and the second image; and   analyzing the difference to generate the predicted value indicating the endometrium receptivity of the endometrium.   
     
     
         24 . (canceled) 
     
     
         25 . (canceled) 
     
     
         26 . (canceled) 
     
     
         27 . (canceled) 
     
     
         28 . The method of  claim 17 , wherein the neural network is trained based on a set of training data comprising:
 a plurality of ultrasound images of one or more endometria, each of the plurality of ultrasound images showing a respective endometrium; and   for each of the plurality of ultrasound images, a respective label indicating an outcome of a respective embryo implantation in the respective endometrium in the respective ultrasound image.   
     
     
         29 . The method of  claim 28 , wherein each of the plurality of ultrasound images is associated with training data comprising a blastocyst quality of an embryo transferred into a respective endometrial cavity in the respective ultrasound image. 
     
     
         30 . (canceled) 
     
     
         31 . A non-transitory computer-readable medium having stored thereon machine interpretable instructions which, when executed by a processor, cause the processor to perform:
 maintaining a data set representing a neural network having a plurality of weights;   obtaining a first image of an endometrium with a first timestamp;   extracting a first set of target endometrium features from the first image; and   generating, using the neural network and based on the first set of target endometrium features, a predicted value indicating a endometrium receptivity of the endometrium in the first image.